Efficient Sparse Large-Scale Multiobjective Optimization Based on Cross-Scale Knowledge Fusion

Due to the curse of dimensionality and the unknown sparsity of search spaces, evolutionary algorithms face immense challenges in approximating optimal solutions for widely studied sparse large-scale multiobjective optimization problems (SLMOPs). Most bilevel encoding scheme (BLES)-based algorithms primarily focus on exploring sparsity in the binary layer, neglecting the real layer. Moreover, the interactions between two layers may be disregarded in these algorithms, thus the latent gap between the two encoding scales could lead to evolutionary ambiguity and performance limitations. To tackle the above issues, this article proposes a novel BLES-based collaborative algorithm using cross-scale knowledge fusion for SLMOPs. The algorithm integrates dual grouping and dual dimension reduction techniques via two subpopulations in a coevolutionary manner. Additionally, the interaction strategy is designed for each technique, leveraging the binary layer to guide the real layer, thus facilitating sufficient cross-scale cooperation. Extensive experiments on benchmark SLMOPs and four real-world applications validate the proposed algorithm's strong competitiveness in solving SLMOPs compared to state-of-the-art algorithms.